Financial Technology

Module 0

Author
Affiliation

Prof. Matthew G. Son

University of South Florida

Financial Technology

What is FinTech?

  • The verbatim definition: Financial Technology

FinTech

  • The buzzword FinTech started to appear around 2015

  • Google Search Trend

  • FinTech has its own specific connotation regarding:

    • Business model

    • Area of Finance

    • Technologies applied in Finance

FinTech by Business Model

  • FinTech companies expands the customer base by:

    • Democratizing technology

      • through use of mobile apps (e.g. Robinhood, Chime)

    • Large cost-reduction with innovative/streamlined business model

      • through automated models based on big data
      • can have large customer base compared to size of the firm

  • B2B, B2C oriented -> P2P, Platform

FinTech by Financial Sector

  • Payment systems, Brokerage/Exchange services (Platform)

    • Paypal (Venmo), Zelle

    • Robinhood, Coinbase

  • Investments and Portfolio management

    • Robo-advising (Portfolio management)

    • High-Frequency, Algorithmic Trading (Mutual/Hedge Funds)

  • Credits and Loans (Banking)

    • Crowdfunding, P2P lending (LendingClub)

FinTech by Technology

The financial companies that use innovative technologies

  • Big data
    • by volume & velocity: large, high frequency data
    • by variety: image, sound, text data
  • Machine Learning
  • Blockchains/ Cryptocurrencies
  • Internet of Things
  • Virtual Reality / Metaverse

Peek at FinTech Business models

Sample start-up companies and their business models:

  1. Numerai
    • Hedge fund leveraging crowdsourced models and big data
  2. Zest AI
    • Credit risk analysis and automated underwriting with alternative data
  3. Niural
    • global team hiring, tax compliance and payments

TechFin

FinTech in the Globe

FinTech in Tampa & St. Pete

Our strategic partnership program to grow network of innovators in FinTech world:

  • https://www.tampabaywave.org/

Introduction to AI

AI, ML, DL

Artificial Intelligence

Artificial Intelligence (AI) : intelligent machine

Those machines are called intelligent as:

  • They have decision-making capabilities

  • like human beings (i.e., “smart”)

Chess and AI

Deep Blue vs. Gary Kasparov (1997)

  • IBM’s Deep Blue defeats world chess champion Kasparov.
  • Marked a milestone in AI with rule-based systems and brute-force calculations.

Programming and ML

Machine Learning

Machine Learning (ML): A subset of AI

  • A technology to allow computer programs to learn the patterns and rules by improving through experience

Algorithms make the computer to learn

  • Some from Statistics, others from Computer science

  • Neural network (Deep Learning) one of such algorithm

Programming and ML

Go (Baduk) and AI

AlphaGo vs. Lee Sedol (2016)

  • AlphaGo defeats Go legend Lee.
  • AlphaGo Zero (2017): Self-learning reinforcement model that surpassed human expertise.

AI and ML examples

AI (Not ML):

  • Traditional Smart technologies
  • Rule-based system
  • Automation
  • Timers (IoT)
  • Grammar/syntax checker (earlier)

ML - Data-driven system - Tesla Autopilot - Image classification - Anomaly detection - Generative AI

Finance example: Loans

If loan officers applied certain known rules for loan decisions:

  • this process can be automated (programmatically, AI)

If we do not know what rules are used, but the outcomes:

  • we could use ML to learn the rules

ML can improve upon the rules for accepting or rejecting loans for:

  • risk minimization / profit maximization

Limits of ML

Complexity-Interpretability Tradeoff

Interpretability Issues

Black Box

  • Hard to explain why a specific decision (e.g., loan approval) was made
  • Financial regulators and institutions require clear reasoning behind decisions

Example: Discrimination in Loans

  • Historical biases in datasets (e.g., lending data) can perpetuate discrimination
  • Example: Lower loan approvals for certain demographics due to biased data.

However, it is difficult to identify and correct (debug) errors or biases in complex ML models.

AI: The 4th Industrial Revolution

How will it change the world?

AI Dystopia / Utopia

We never know, but

Some insights from how AI shaped the world of Go

  • One of the first field that was disrupted by AI
  • A game of longest human history (BC 2200)
  • A perfect information game, solvable in theory

When AI Disrupts Human

Corner opening moves (joseki)

  • AI challenged some traditional, long-accepted patterns (found suboptimal)

  • No longer played (deprecated)

  • Play focus: imaginative to calculative

    • Convergence of player’s style (meta-game)

When AI can’t Replace Human

  • Human Matters

    • Nobody wants to watch machine games
    • Sentiment outweighs accuracy/efficiency

  • Simplicity (interpretability) vs Accuracy tradeoff

    • Extreme complexity: too hard to understand

When AI Empowers Human

Mostly explains the AI change in Go field.

  • Many traditional moves are confirmed correct/revised instead

    • AI confirms and strengthens human theory

  • New AI-based josekis are found and adopted

    • Players learn from AI’s playstyle

    • Commentators use AI for win rate

Insights for Finance

In comparison, the field of Finance is

  • A game of incomplete / imperfect information.

Insights for Finance

  1. Financial Accountability:

  • High scrutiny fields: like banking, auditing, and compliance that need transparency will less likely be impacted

  • Innovation-friendly: asset management, quantitative trading will more likely impacted

  1. Homogenized investment strategy:

  • Less distinctive portfolio styles / Portfolio herding

  • Potentially leading to higher systemic risks

Insights for Finance in the age of AI

  1. Soft Information Matters:

  • Qualitative (big) data will be crucial for complementing hard-data

  • A competitive edge for firms that efficiently process alternative data

  • Appreciation of Proprietary data value